Improved Oral Cancer Classification Using Hybridization of Deep Feature and Correlation-based Feature Weight
Year: 2024
Type: article
Abstract: This study presents a hybrid deep learning-based feature classification framework for binary cancer detection using medical images. The proposed method extracts heterogeneous deep features from ResNet18 and EfficientNet-B0, which are then concatenated to form a unified feature vector. To quantify feature relevance, the Pearson correlation between each feature dimension and the ground-truth labels is computed. These correlation scores are then used to weight each feature dimension, producing a correlation-enhanced representation. The weighted feature matrix is then used to train multiple classifiers, including Random Forest, SVM, XGBoost, Extra Trees, and Logistic Regression.
Source: Cuestiones de Fisioterapia
DOI: 10.48047/hqkefd72
Authors: Nidhi Agrawal, Yogesh Kumar Rathore
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412685541